Scientific Leads:
Ferraresi Virginia, Dionisi Francesco, Marzi Simona, Tocci Piera

Description

This research line is characterised by a strong focus on technological innovation aimed at defining new diagnostic technologies and novel combined therapeutic approaches for the conduct of, and participation in, interventional clinical trials. The development and use of digital tools capable of integrating and analysing large volumes of clinical and research data from multiple sources, in order to assess and monitor the long-term real-world outcomes of anticancer therapies, represent an additional area of application. Improving the prognosis of patients with both highly prevalent and rare tumours - for which IRE has developed specific expertise over time and plays a recognised role within the EURACAN European network - requires the integration of synergistic and complementary expertise across the entire diagnostic and therapeutic pathway. Increasingly personalised therapies based on individual patient characteristics have become possible through: a) technological advances in biomolecular research and the development of state-of-the-art in vitro and in vivo preclinical models, with particular emphasis on organotypic models such as organoids and tumoroids, which more faithfully recapitulate tumours and their surrounding microenvironment; b) the integration of radiomics and radiogenomics data in imaging; c) minimally invasive surgery; d) advances in radiotherapy, including treatment planning and the potential future use of specialised techniques such as proton therapy; and e) the use of molecularly targeted treatments in medical oncology. Radiomics, in particular, is a rapidly expanding field of major relevance in oncology. The extraction of large volumes of quantitative data from computed tomography, magnetic resonance imaging, mammography, PET/CT and ultrasonography enables accurate and non-invasive characterisation of tissues and tumours that cannot be achieved through visual image assessment alone. This requires strict adherence to rigorous standards, recently defined by international guidelines, to ensure the reproducibility and robustness of complex radiomics methodologies. Subsequent integration with data capturing the molecular and genomic characteristics of tissues can further enhance these diagnostic tools, with the aim of building integrated radiogenomics datasets to support the development of prognostic and predictive models for clinical decision-making. In this context, digital and artificial-intelligence tools are essential for the integration and interpretation of big data from multiple sources, including public databases, electronic health records, diagnostic data, clinical and translational research data, and advanced preclinical models. The development of these models requires dedicated data centres and the application of business-intelligence methods. As no international consensus has yet been reached on the most appropriate artificial-intelligence tools for the development of classification or regression models, one of the objectives of this research line is to contribute to the definition of the most suitable methodological, statistical and computational approaches according to the specific nature of the data under investigation.

Objectives

1. To develop state-of-the-art patient-derived preclinical models, including primary-cell cultures and tumour spheroids, three-dimensional organotypic models such as organoids and tumoroids - incorporating tumour, stromal and immune components, as well as the extracellular matrix, and thus more faithfully recapitulating the complexity of the tumour ecosystem - and murine models, including patient-derived xenografts (PDXs) and syngeneic models. These models will be used to identify novel therapeutic targets and develop drug combinations potentially capable of interfering with tumour progression and reprogramming the tumour microenvironment, with particular emphasis on the repurposing for oncology of drugs already approved in clinical practice for non-oncological conditions.
2. To introduce into routine diagnostics an omics-based analytical system integrating genomic, proteomic, metabolomic and functional tests, capable of providing multilevel, patient-specific characterisation.
3. To develop innovative therapeutic approaches through the use and implementation of advanced technologies, including robotic and minimally invasive surgery, radiotherapy, proton therapy, radiosurgery, stereotactic body radiation therapy (SBRT), and nuclear medicine therapy. 
4. To develop business-intelligence models for the integrated analysis of clinical